The BETA database

The brain-computer interface (BCI) provides an alternative means to communicate and it has sparked growing interest in the past two decades. Specifically, for Steady-State Visual Evoked Potential (SSVEP) based BCI, marked improvement has been made in the frequency recognition method and data sharing. However, the number of pubic databases is still limited in this field. Therefore, we present a \textbf{BE}nchmark database \textbf{T}owards BCI \textbf{A}pplication (BETA) in the study. The BETA database is composed of 64-channel Electroencephalogram (EEG) data of 70 subjects performing a 40-target cued-spelling task. The design and the acquisition of the BETA is in pursuit of meeting the demand from real-world applications and it can be used as a test-bed for these scenarios. We validate the database by a series of analyses and conduct the classification analysis of eleven frequency recognition methods on BETA. We recommend to use the metric of wide-band signal-to-noise ratio (SNR) and BCI quotient to characterize the SSVEP at the single-trial and population levels, respectively. The BETA database has an alternative download website link of http://bci.med.tsinghua.edu.cn/download.html.

脑机接口（Brain-Computer Interface，BCI）为通信提供了一种替代途径，在过去二十年中引发了日益增长的研究兴趣。具体而言，针对基于稳态视觉诱发电位（Steady-State Visual Evoked Potential，SSVEP）的脑机接口，其频率识别方法与数据共享领域已取得显著进展。然而该领域内可公开获取的数据库数量仍然有限。因此，本研究构建了面向脑机接口应用的基准数据库（Benchmark Towards BCI Application，BETA）。该BETA数据库包含70名受试者完成40目标线索拼写任务时采集的64通道脑电图（Electroencephalogram，EEG）数据。BETA数据库的设计与采集旨在满足实际应用场景的需求，可作为此类场景的测试平台。本研究通过一系列分析对该数据库进行了验证，并针对BETA数据库开展了11种频率识别方法的分类分析。我们建议分别采用宽带信噪比（Signal-to-Noise Ratio，SNR）与脑机接口商值（BCI Quotient）两个指标，在单试次与群体水平上表征稳态视觉诱发电位。该BETA数据库的备用下载链接为http://bci.med.tsinghua.edu.cn/download.html。

搜集汇总

背景与挑战

背景概述

BETA database是一个包含70名受试者64通道EEG数据的基准数据库，专注于稳态视觉诱发电位（SSVEP）的脑机接口研究，旨在支持实际应用场景和算法测试。数据集还提供了频率识别方法的分类分析和推荐评估指标。